RobustStatisticsSegmentation

Robust Statistics Based Segmentation

How to get the module

1. start Slicer 3.5
2. open View->Extension Manager
3. check "Find & Install", click "Next"
4. in the list, select "LocalRegionalSegModule", then "Download & Install", and "Next"
5. Slicer will ask to restart, confirm.
6. after restarts, the module is in Module category: "Segmentation-->Localized Region-based Segmentation"

Usage

---> --->

• Note:

1. The Approximate volume is just a rough upper limit for the volume. It should be at least the size of the object. This is because when the volume reaches that, the program must stop. However, other criteria may stop the algorithm before the volume reaches this value.
2. The fiducial points can be thrown into the object. What I do is I just add two fiducial points and move them into the object within one slice.

Testing

Several tests are conducted and shown here along with the parameters used to get the results.

Testing case: left kidney

• Data set: http://wiki.na-mic.org/Wiki/images/8/8d/Patient1.tar.gz
• Approximate volume: 200 mL
• Intensity homogeneity: 0.1
• Boundary smoothness: 0.5

Testing case: right kidney

• Data set: http://wiki.na-mic.org/Wiki/images/8/8d/Patient1.tar.gz
• Approximate volume: 200 mL
• Intensity homogeneity: 0.1
• Boundary smoothness: 0.5

In the figure below we show the position of the two seeds. They are both in the cortex region. And it can be seen in the segmentation result, the renal cortex got segmented and the pelvis is not.

Testing case: tumor

• Data set: http://wiki.na-mic.org/Wiki/images/0/0f/MayExperiments.zip (DiffusionEditorBaselineNode.nrrd)
• Approximate volume: 30 mL
• Intensity homogeneity: 0.1

Testing case: tumor

• Data set: http://www.spl.harvard.edu/publications/bitstream/download/4217 (case3/grayscale.nrrd)
• Approximate volume: 30 mL
• Intensity homogeneity: 0.4

Testing case: tumor

• Data set: http://www.spl.harvard.edu/publications/bitstream/download/4217 (case5/grayscale.nrrd)
• Approximate volume: 3 mL
• Intensity homogeneity: 0.5
• Boundary smoothness: 0.4

This is a difficult case. All the other methods tested either captures only the middle dark spot, or leaks out of the tumor. But the method here nicely capture both the dark core and the bright shell, without leaking out to regions with intensities in the middle.

Testing case: vervet brain

• Data set: http://www.na-mic.org/Wiki/index.php/Vervet_MRI_registration (http://www.bsl.ece.vt.edu/data/vervet_atlas/VPA-10-1.1.zip Vervet_T1_Template_WholeHead.nii)
• Approximate volume: 15 mL
• Intensity homogeneity: 0.5

Previous tests all run with two fiducial points, and running time are all less than 10 seconds. This case has 6 points and runs for 15min to get the result shown.

Key Investigators

Georgia Tech: Yi Gao and Allen Tannenbaum

BWH: Katie Hayes, Andriy Fedorov, and Ron Kikinis

Publications

The method is based on:

• NA-MIC Publications Database
• Yang, Y. and Tannenbaum, A. and Giddens, D. and Coulter, WH, "Knowledge-based 3D segmentation and reconstruction of coronary arteries using CT images", in IEEE EMBS 2004, pp1664--1666